Gaussian Process Regression-Based Video Anomaly Detection and Localization With Hierarchical Feature Representation

@article{Cheng2015GaussianPR,
  title={Gaussian Process Regression-Based Video Anomaly Detection and Localization With Hierarchical Feature Representation},
  author={Kai-Wen Cheng and Yie-Tarng Chen and Wen-Hsien Fang},
  journal={IEEE Transactions on Image Processing},
  year={2015},
  volume={24},
  pages={5288-5301}
}
This paper presents a hierarchical framework for detecting local and global anomalies via hierarchical feature representation and Gaussian process regression (GPR) which is fully non-parametric and robust to the noisy training data, and supports sparse features. While most research on anomaly detection has focused more on detecting local anomalies, we are more interested in global anomalies that involve multiple normal events interacting in an unusual manner, such as car accidents. To… CONTINUE READING
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  • C. C. Loy, T. Xiang
  • Gong, “Modelling multi-object activity by…
  • 2009
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